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python3\n\"\"\"\n4-model routed MBR submission script for the Deep Past Challenge.\n\nPinned to the current mixed48 replay winner in this repo:\n- variant = selective_public_safe\n- mixed48 local_raw = 35.63049489273052\n- source = reports/pool_dump_dev_mixed_short_long_proxy48_dualall_20260320_v1/replay_sweep_v3/sweep_summary.csv\n\nModel family:\n1. baseline_all: byt5-akkadian-mbr-v2 on all rows\n2. open34x_all: byt5-akkadian-optimized-34x on all rows\n3. main_3039: latest short expert on short rows\n4. long_3039: long expert on long rows\n\nVerified route/decode core:\n- route threshold = 400 bytes\n- default decode = rp 1.1, lp 1.0, early_stopping = True\n- long decode = max_new_tokens 416, min_length 64, rp 1.15, lp 1.2, early_stopping = True\n\nRuntime guards:\n1. Strict startup checks for all required model files.\n2. 4-row Kaggle public test fast-skip for save/run.\n3. Timeout watchdog degrades generation before notebook timeout.\n\nSelective postprocess:\n1. apply safe postprocess only to public branches: baseline_all + open34x_all\n2. keep expert branches main_3039 + long_3039 raw before MBR\n\"\"\"\n\nimport atexit\nimport gc\nimport logging\nimport math\nimport os\nimport re\nimport shutil\nimport statistics\nimport tempfile\nimport time\nfrom collections import Counter\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Dict, List, Sequence\n\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nfrom tqdm.auto import tqdm\nfrom transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n\ntry:\n from contextlib import nullcontext\nexcept ImportError:\n class nullcontext(object):\n def __init__(self, enter_result=None):\n self.enter_result = enter_result\n\n def __enter__(self):\n return self.enter_result\n\n def __exit__(self, exc_type, exc_value, traceback):\n return False\n\ntry:\n import sacrebleu\nexcept ImportError:\n class _Score(object):\n def __init__(self, score):\n self.score = float(score)\n\n def _char_ngrams(text, n):\n return Counter(text[i : i + n] for i in range(max(0, len(text) - n + 1)))\n\n def _token_ngrams(tokens, n):\n return Counter(tuple(tokens[i : i + n]) for i in range(max(0, len(tokens) - n + 1)))\n\n class _FallbackCHRF(object):\n def __init__(self, word_order=2):\n self.word_order = word_order\n\n def sentence_score(self, hyp, refs):\n ref = refs[0] if refs else \"\"\n scores = []\n for n in range(1, 7):\n hyp_ngrams = _char_ngrams(hyp, n)\n ref_ngrams = _char_ngrams(ref, n)\n if not hyp_ngrams or not ref_ngrams:\n continue\n overlap = sum((hyp_ngrams & ref_ngrams).values())\n precision = overlap / float(sum(hyp_ngrams.values()) or 1)\n recall = overlap / float(sum(ref_ngrams.values()) or 1)\n if precision + recall == 0.0:\n scores.append(0.0)\n else:\n scores.append((2.0 * precision * recall) / (precision + recall))\n return _Score(100.0 * (sum(scores) / float(len(scores) or 1)))\n\n class _FallbackBLEU(object):\n def __init__(self, effective_order=True):\n self.effective_order = effective_order\n\n def sentence_score(self, hyp, refs):\n ref = refs[0] if refs else \"\"\n hyp_tokens = hyp.split()\n ref_tokens = ref.split()\n if not hyp_tokens:\n return _Score(0.0)\n max_order = min(4, len(hyp_tokens)) if self.effective_order else 4\n precisions = []\n for n in range(1, max_order + 1):\n hyp_ngrams = _token_ngrams(hyp_tokens, n)\n ref_ngrams = _token_ngrams(ref_tokens, n)\n total = float(sum(hyp_ngrams.values()) or 1.0)\n overlap = float(sum((hyp_ngrams & ref_ngrams).values()))\n precisions.append((overlap + 1.0) / (total + 1.0))\n log_prec = sum(math.log(p) for p in precisions) / float(len(precisions) or 1)\n bp = 1.0\n if len(hyp_tokens) < len(ref_tokens):\n bp = math.exp(1.0 - (len(ref_tokens) / float(len(hyp_tokens) or 1)))\n return _Score(100.0 * bp * math.exp(log_prec))\n\n class _MetricsCompat(object):\n CHRF = _FallbackCHRF\n BLEU = _FallbackBLEU\n\n class _SacreBleuCompat(object):\n metrics = _MetricsCompat()\n\n sacrebleu = _SacreBleuCompat()\n\n\nos.environ.setdefault(\"OMP_NUM_THREADS\", \"4\")\nos.environ.setdefault(\"MKL_NUM_THREADS\", \"4\")\nos.environ.setdefault(\"TOKENIZERS_PARALLELISM\", \"true\")\n\nTMP_TOKENIZER_DIRS = []\nPROMPT_PREFIX = \"translate Akkadian to English: \"\nGLOBAL_START_TIME = time.time()\nTIMEOUT_THRESHOLD_SEC = float(os.getenv(\"AKK_TIMEOUT_THRESHOLD_SEC\", str(8.25 * 3600)))\nPUBLIC_SAFE_LABELS = {\"baseline_all\", \"open34x_all\"}\n\n\ndef cleanup_tmp_tokenizers():\n for path in TMP_TOKENIZER_DIRS:\n try:\n shutil.rmtree(str(path), ignore_errors=True)\n except Exception:\n pass\n\n\natexit.register(cleanup_tmp_tokenizers)\n\n\ndef setup_logger():\n logger = logging.getLogger(\"routed_4model_mbr\")\n logger.setLevel(logging.INFO)\n if not logger.handlers:\n handler = logging.StreamHandler()\n handler.setFormatter(logging.Formatter(\"%(asctime)s | %(levelname)s | %(message)s\"))\n logger.addHandler(handler)\n return logger\n\n\nLOGGER = setup_logger()\n\n\ndef is_time_running_out():\n return (time.time() - GLOBAL_START_TIME) > TIMEOUT_THRESHOLD_SEC\n\n\ndef resolve_existing_path(candidates: Sequence[str]) -> str:\n for path in candidates:\n if path and Path(path).exists():\n return path\n return candidates[0]\n\n\ndef find_weight_file(model_dir: Path) -> Path:\n for name in (\"model.safetensors\", \"pytorch_model.bin\", \"flax_model.msgpack\"):\n candidate = model_dir / name\n if candidate.exists():\n return candidate\n return model_dir / \"model.safetensors\"\n\n\ndef validate_model_dir(label: str, model_dir: str):\n model_path = Path(model_dir)\n missing = []\n if not model_path.exists():\n raise FileNotFoundError(\"{} directory is missing: {}\".format(label, model_path))\n if not model_path.is_dir():\n raise FileNotFoundError(\"{} is not a directory: {}\".format(label, model_path))\n\n required_files = [\n model_path / \"config.json\",\n model_path / \"tokenizer_config.json\",\n model_path / \"special_tokens_map.json\",\n ]\n for file_path in required_files:\n if not file_path.exists():\n missing.append(str(file_path))\n\n weight_file = find_weight_file(model_path)\n if not weight_file.exists():\n missing.append(\n \"{} (expected one of model.safetensors / pytorch_model.bin / flax_model.msgpack)\".format(model_path)\n )\n\n if missing:\n raise FileNotFoundError(\n \"{} is incomplete. Missing files: {}\".format(label, \" | \".join(missing))\n )\n\n\n@dataclass(frozen=True)\nclass DecodeConfig:\n max_new_tokens: int\n min_length: int = 0\n repetition_penalty: float = 1.0\n length_penalty: float = 1.0\n early_stopping: bool = True\n\n\n@dataclass\nclass KaggleConfig:\n test_data_path: str = \"/kaggle/input/deep-past-initiative-machine-translation/test.csv\"\n sample_sub_path: str = \"/kaggle/input/deep-past-initiative-machine-translation/sample_submission.csv\"\n output_dir: str = \"/kaggle/working/\"\n route_debug_path: str = \"/kaggle/working/route_debug.csv\"\n\n baseline_model: str = \"\"\n open_model: str = \"\"\n main_model: str = \"\"\n long_model: str = \"\"\n\n route_threshold_bytes: int = 400\n public_test_fast_skip_rows: int = 4\n enable_public_test_fast_skip: bool = os.getenv(\"AKK_ENABLE_PUBLIC_TEST_FAST_SKIP\", \"1\").strip() != \"0\"\n force_full_inference: bool = os.getenv(\"AKK_FORCE_FULL_INFERENCE\", \"0\").strip() == \"1\"\n max_input_length: int = 512\n batch_size: int = 2 if torch.cuda.is_available() else 1\n num_workers: int = 2\n\n default_max_new_tokens: int = 384\n default_min_length: int = 0\n default_repetition_penalty: float = 1.1\n default_length_penalty: float = 1.0\n default_early_stopping: bool = True\n\n long_max_new_tokens: int = 416\n long_min_length: int = 64\n long_repetition_penalty: float = 1.15\n long_length_penalty: float = 1.2\n long_early_stopping: bool = True\n\n num_beam_cands: int = 4\n num_sample_cands: int = 8\n mbr_pool_cap: int = 48\n\n def __post_init__(self):\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n Path(self.output_dir).mkdir(parents=True, exist_ok=True)\n try:\n self.use_bf16 = bool(self.device.type == \"cuda\" and torch.cuda.is_bf16_supported())\n except Exception:\n self.use_bf16 = False\n\n baseline_candidates = [\n os.getenv(\"AKK_BASELINE_MODEL_PATH\", \"\").strip(),\n \"/kaggle/input/models/mattiaangeli/byt5-akkadian-mbr-v2/pytorch/default/1\",\n ]\n open_candidates = [\n os.getenv(\"AKK_OPEN_MODEL_PATH\", \"\").strip(),\n \"/kaggle/input/final-byt5/byt5-akkadian-optimized-34x\",\n ]\n main_candidates = [\n os.getenv(\"AKK_MAIN_MODEL_PATH\", \"\").strip(),\n \"/kaggle/input/datasets/bianfu/short-model-v2/rw00028_short_model_20260320\",\n \"/kaggle/input/datasets/bianfu/2model-v1/main_model\",\n ]\n long_candidates = [\n os.getenv(\"AKK_LONG_MODEL_PATH\", \"\").strip(),\n \"/kaggle/input/datasets/bianfu/2model-v1/long_model\",\n ]\n self.baseline_model = resolve_existing_path([x for x in baseline_candidates if x])\n self.open_model = resolve_existing_path([x for x in open_candidates if x])\n self.main_model = resolve_existing_path([x for x in main_candidates if x])\n self.long_model = resolve_existing_path([x for x in long_candidates if x])\n\n def default_decode_cfg(self):\n return DecodeConfig(\n max_new_tokens=self.default_max_new_tokens,\n min_length=self.default_min_length,\n repetition_penalty=self.default_repetition_penalty,\n length_penalty=self.default_length_penalty,\n early_stopping=self.default_early_stopping,\n )\n\n def long_decode_cfg(self):\n return DecodeConfig(\n max_new_tokens=self.long_max_new_tokens,\n min_length=self.long_min_length,\n repetition_penalty=self.long_repetition_penalty,\n length_penalty=self.long_length_penalty,\n early_stopping=self.long_early_stopping,\n )\n\n\n_GAP_UNIFIED_RE = re.compile(\n r\"<\\s*big[\\s_\\-]*gap\\s*>|<\\s*gap\\s*>|\\bbig[\\s_\\-]*gap\\b|\\bx(?:\\s+x)+\\b|\\.{3,}|\\[\\.+\\]|\\[\\s*x\\s*\\]|\\(\\s*x\\s*\\)|(?]*>>|<(?!gap\\b)[^>]*>\")\n_WS_RE = re.compile(r\"\\s+\")\n_REPEAT_WORD_RE = re.compile(r\"\\b(\\w+)(?:\\s+\\1\\b)+\", re.I)\n\n\ndef _month_repl(match):\n return \"Month {}\".format(_ROMAN2INT.get(match.group(1).upper(), match.group(1)))\n\n\nclass SafePostprocessor:\n def process(self, text):\n s = str(text or \"\")\n s = _GAP_UNIFIED_RE.sub(\"\", s)\n s = _PN_RE.sub(\"\", s)\n for pattern, repl in _SHEKEL_REPLS:\n s = pattern.sub(repl, s)\n s = _EXACT_FRAC_RE.sub(lambda m: _EXACT_FRAC_MAP[m.group(1)], s)\n s = _SOFT_GRAM_RE.sub(\" \", s)\n s = _UNCERTAIN_RE.sub(\"\", s)\n s = _STRAY_MARKS_RE.sub(\"\", s)\n s = _CURLY_DQ_RE.sub('\"', s)\n s = _CURLY_SQ_RE.sub(\"'\", s)\n s = _MONTH_RE.sub(_month_repl, s)\n s = s.replace(\"\", \" __GAP__ \")\n s = _REPEAT_WORD_RE.sub(r\"\\1\", s)\n for n in range(4, 1, -1):\n s = re.sub(\n r\"\\b((?:\\w+\\s+){\" + str(n - 1) + r\"}\\w+)(?:\\s+\\1\\b)+\",\n r\"\\1\",\n s,\n flags=re.I,\n )\n s = s.replace(\" __GAP__ \", \"\")\n return _WS_RE.sub(\" \", s).strip()\n\n\nclass LinearMBRSelector:\n def __init__(self, pool_cap=48):\n self.pool_cap = pool_cap\n self._chrf = sacrebleu.metrics.CHRF(word_order=2)\n self._bleu = sacrebleu.metrics.BLEU(effective_order=True)\n self.w_chrf = 0.55\n self.w_bleu = 0.25\n self.w_jaccard = 0.20\n self.w_length = 0.10\n self._pw_total = self.w_chrf + self.w_bleu + self.w_jaccard\n\n def _score(self, a, b):\n if not a or not b:\n return 0.0\n chrf_val = float(self._chrf.sentence_score(a, [b]).score)\n try:\n bleu_val = float(self._bleu.sentence_score(a, [b]).score)\n except Exception:\n bleu_val = 0.0\n ta = set(a.lower().split())\n tb = set(b.lower().split())\n if ta and tb:\n jaccard_val = 100.0 * len(ta & tb) / len(ta | tb)\n elif not ta and not tb:\n jaccard_val = 100.0\n else:\n jaccard_val = 0.0\n return (\n self.w_chrf * chrf_val\n + self.w_bleu * bleu_val\n + self.w_jaccard * jaccard_val\n ) / self._pw_total\n\n def pick(self, candidates):\n seen = set()\n deduped = []\n for cand in candidates:\n cand = str(cand or \"\").strip()\n if cand and cand not in seen:\n seen.add(cand)\n deduped.append(cand)\n deduped = deduped[: self.pool_cap]\n count = len(deduped)\n if count == 0:\n return \"The tablet is too damaged to translate.\"\n if count == 1:\n return deduped[0]\n\n lengths = [len(x.split()) for x in deduped]\n median_len = float(statistics.median(lengths))\n sigma = max(median_len * 0.4, 5.0)\n\n best_score = -1e9\n best_text = deduped[0]\n for idx, cand in enumerate(deduped):\n pairwise = sum(self._score(cand, other) for j, other in enumerate(deduped) if j != idx) / max(1, count - 1)\n z = (lengths[idx] - median_len) / sigma\n length_bonus = 100.0 * math.exp(-0.5 * z * z)\n final_score = pairwise + self.w_length * length_bonus\n if final_score > best_score:\n best_score = final_score\n best_text = cand\n return best_text\n\n\nclass AkkadianDataset(Dataset):\n def __init__(self, df):\n self.ids = df[\"id\"].tolist()\n self.texts = [PROMPT_PREFIX + str(text) for text in df[\"transliteration\"].tolist()]\n\n def __len__(self):\n return len(self.ids)\n\n def __getitem__(self, idx):\n return self.ids[idx], self.texts[idx]\n\n\ndef safe_load_tokenizer(model_dir):\n model_dir_path = Path(model_dir)\n try:\n return AutoTokenizer.from_pretrained(str(model_dir_path), local_files_only=True)\n except Exception as exc:\n if \"Non-consecutive added token\" not in str(exc):\n raise\n\n tmp_dir = Path(tempfile.mkdtemp(prefix=\"tokfix_\"))\n TMP_TOKENIZER_DIRS.append(tmp_dir)\n for item in model_dir_path.iterdir():\n if item.name == \"added_tokens.json\":\n continue\n dst = tmp_dir / item.name\n if item.is_dir():\n shutil.copytree(str(item), str(dst))\n else:\n shutil.copy2(str(item), str(dst))\n return AutoTokenizer.from_pretrained(str(tmp_dir), local_files_only=True)\n\n\ndef safe_load_model(model_dir):\n model_dir_path = Path(model_dir)\n try:\n return AutoModelForSeq2SeqLM.from_pretrained(str(model_dir_path), local_files_only=True)\n except OSError as exc:\n flax_weights = model_dir_path / \"flax_model.msgpack\"\n if flax_weights.exists() and \"Flax weights\" in str(exc):\n return AutoModelForSeq2SeqLM.from_pretrained(\n str(model_dir_path),\n from_flax=True,\n local_files_only=True,\n )\n raise\n\n\nclass SafeModelWrapper:\n def __init__(self, model_path, cfg, label):\n self.cfg = cfg\n self.label = label\n LOGGER.info(\"[%s] Loading %s\", label, model_path)\n self.tokenizer = safe_load_tokenizer(model_path)\n self.model = safe_load_model(model_path).to(cfg.device).eval()\n if cfg.device.type == \"cuda\":\n torch.cuda.empty_cache()\n LOGGER.info(\"[%s] GPU allocated %.2f GB\", label, torch.cuda.memory_allocated() / 1e9)\n\n def collate(self, batch):\n ids = [item[0] for item in batch]\n texts = [item[1] for item in batch]\n enc = self.tokenizer(\n texts,\n max_length=self.cfg.max_input_length,\n padding=True,\n truncation=True,\n return_tensors=\"pt\",\n )\n return ids, enc\n\n def generate_candidates(self, input_ids, attention_mask, decode_cfg):\n batch_size = input_ids.shape[0]\n if self.cfg.use_bf16:\n amp_ctx = torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\n else:\n amp_ctx = nullcontext()\n\n if is_time_running_out():\n beam_cands = 1\n sample_cands = 0\n LOGGER.warning(\"[%s] timeout fallback: greedy-only mode\", self.label)\n else:\n beam_cands = self.cfg.num_beam_cands\n sample_cands = self.cfg.num_sample_cands\n\n pools = [[] for _ in range(batch_size)]\n with amp_ctx:\n beam_outputs = self.model.generate(\n input_ids=input_ids,\n attention_mask=attention_mask,\n do_sample=False,\n num_beams=max(4, beam_cands),\n num_return_sequences=beam_cands,\n max_new_tokens=decode_cfg.max_new_tokens,\n min_length=decode_cfg.min_length,\n repetition_penalty=decode_cfg.repetition_penalty,\n length_penalty=decode_cfg.length_penalty,\n early_stopping=decode_cfg.early_stopping,\n use_cache=True,\n )\n beam_texts = self.tokenizer.batch_decode(beam_outputs, skip_special_tokens=True)\n for idx in range(batch_size):\n pools[idx].extend(beam_texts[idx * beam_cands : (idx + 1) * beam_cands])\n\n if sample_cands > 0:\n sample_outputs = self.model.generate(\n input_ids=input_ids,\n attention_mask=attention_mask,\n do_sample=True,\n top_p=0.9,\n temperature=0.8,\n num_beams=1,\n num_return_sequences=sample_cands,\n max_new_tokens=decode_cfg.max_new_tokens,\n min_length=decode_cfg.min_length,\n repetition_penalty=decode_cfg.repetition_penalty,\n early_stopping=decode_cfg.early_stopping,\n use_cache=True,\n )\n sample_texts = self.tokenizer.batch_decode(sample_outputs, skip_special_tokens=True)\n for idx in range(batch_size):\n pools[idx].extend(sample_texts[idx * sample_cands : (idx + 1) * sample_cands])\n\n return pools\n\n def unload(self):\n del self.model\n del self.tokenizer\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n LOGGER.info(\"[%s] Unloaded\", self.label)\n\n\ndef utf8_len(text):\n return len(str(text).encode(\"utf-8\"))\n\n\ndef process_subset(subset_df, model_path, label, decode_cfg, cfg, master_pools):\n if subset_df.empty:\n LOGGER.info(\"[%s] subset empty, skip\", label)\n return\n wrapper = SafeModelWrapper(model_path, cfg, label)\n dataset = AkkadianDataset(subset_df)\n loader = DataLoader(\n dataset,\n batch_size=cfg.batch_size,\n shuffle=False,\n num_workers=cfg.num_workers,\n collate_fn=wrapper.collate,\n )\n try:\n with torch.inference_mode():\n for batch_ids, enc in tqdm(loader, desc=\"Generating {}\".format(label)):\n input_ids = enc.input_ids.to(cfg.device)\n attention_mask = enc.attention_mask.to(cfg.device)\n batch_pools = wrapper.generate_candidates(input_ids, attention_mask, decode_cfg)\n for sample_id, pool in zip(batch_ids, batch_pools):\n master_pools[str(sample_id)].extend(\n {\"label\": label, \"text\": str(text)}\n for text in pool\n )\n finally:\n wrapper.unload()\n\n\ndef write_route_debug(df, cfg):\n route_df = df.copy()\n route_df[\"route\"] = [\n \"long_expert\" if src_len > cfg.route_threshold_bytes else \"short_expert\"\n for src_len in route_df[\"src_len\"].tolist()\n ]\n route_df[\"baseline_model\"] = Path(cfg.baseline_model).name\n route_df[\"open_model\"] = Path(cfg.open_model).name\n route_df[\"main_model\"] = Path(cfg.main_model).name\n route_df[\"long_model\"] = Path(cfg.long_model).name\n route_df.to_csv(cfg.route_debug_path, index=False)\n LOGGER.info(\"Route debug saved to %s\", cfg.route_debug_path)\n\n\ndef ensure_startup_files_exist(cfg):\n required_paths = {\n \"test_data_path\": cfg.test_data_path,\n \"sample_sub_path\": cfg.sample_sub_path,\n }\n missing = []\n for key, path_str in required_paths.items():\n if not Path(path_str).exists():\n missing.append(\"{}={}\".format(key, path_str))\n if missing:\n raise FileNotFoundError(\"Missing required startup files: {}\".format(\" | \".join(missing)))\n validate_model_dir(\"baseline_model\", cfg.baseline_model)\n validate_model_dir(\"open_model\", cfg.open_model)\n validate_model_dir(\"main_model\", cfg.main_model)\n validate_model_dir(\"long_model\", cfg.long_model)\n\n\ndef should_fast_skip_public_test(df, cfg):\n if cfg.force_full_inference:\n return False\n if not cfg.enable_public_test_fast_skip:\n return False\n return len(df) == cfg.public_test_fast_skip_rows\n\n\ndef build_fast_skip_rows(df):\n fallback_text = \"Public test fast-skip mode.\"\n return [{\"id\": sample_id, \"translation\": fallback_text} for sample_id in df[\"id\"].tolist()]\n\n\ndef write_fast_skip_route_debug(df, cfg):\n route_df = df.copy()\n route_df[\"route\"] = \"public_test_fast_skip\"\n route_df[\"baseline_model\"] = \"skipped\"\n route_df[\"open_model\"] = \"skipped\"\n route_df[\"main_model\"] = \"skipped\"\n route_df[\"long_model\"] = \"skipped\"\n route_df.to_csv(cfg.route_debug_path, index=False)\n LOGGER.info(\"Fast-skip route debug saved to %s\", cfg.route_debug_path)\n\n\ndef run_pipeline():\n cfg = KaggleConfig()\n LOGGER.info(\"=\" * 60)\n LOGGER.info(\"4-model routed MBR submission\")\n LOGGER.info(\"device=%s bf16=%s\", cfg.device, cfg.use_bf16)\n LOGGER.info(\"baseline=%s\", cfg.baseline_model)\n LOGGER.info(\"open=%s\", cfg.open_model)\n LOGGER.info(\"main=%s\", cfg.main_model)\n LOGGER.info(\"long=%s\", cfg.long_model)\n LOGGER.info(\"route threshold=%s bytes\", cfg.route_threshold_bytes)\n LOGGER.info(\"timeout threshold sec=%s\", TIMEOUT_THRESHOLD_SEC)\n LOGGER.info(\"=\" * 60)\n\n ensure_startup_files_exist(cfg)\n df = pd.read_csv(cfg.test_data_path)\n if should_fast_skip_public_test(df, cfg):\n LOGGER.warning(\n \"Detected %d-row public test file. Fast-skip path enabled. Set AKK_FORCE_FULL_INFERENCE=1 to disable.\",\n len(df),\n )\n out_path = Path(cfg.output_dir) / \"submission.csv\"\n pd.DataFrame(build_fast_skip_rows(df)).to_csv(out_path, index=False)\n write_fast_skip_route_debug(df, cfg)\n LOGGER.info(\"submission saved to %s\", out_path)\n return\n\n df[\"src_len\"] = df[\"transliteration\"].apply(utf8_len)\n short_df = df[df[\"src_len\"] <= cfg.route_threshold_bytes].copy()\n long_df = df[df[\"src_len\"] > cfg.route_threshold_bytes].copy()\n\n LOGGER.info(\"total=%d short=%d long=%d\", len(df), len(short_df), len(long_df))\n write_route_debug(df, cfg)\n\n master_pools = {str(sample_id): [] for sample_id in df[\"id\"].tolist()}\n\n process_subset(df, cfg.baseline_model, \"baseline_all\", cfg.default_decode_cfg(), cfg, master_pools)\n process_subset(df, cfg.open_model, \"open34x_all\", cfg.default_decode_cfg(), cfg, master_pools)\n process_subset(short_df, cfg.main_model, \"main_3039\", cfg.default_decode_cfg(), cfg, master_pools)\n process_subset(long_df, cfg.long_model, \"long_3039\", cfg.long_decode_cfg(), cfg, master_pools)\n\n processor = SafePostprocessor()\n mbr = LinearMBRSelector(pool_cap=cfg.mbr_pool_cap)\n LOGGER.info(\"Selective safe labels=%s\", \",\".join(sorted(PUBLIC_SAFE_LABELS)))\n final_rows = []\n for sample_id in tqdm(df[\"id\"].tolist(), desc=\"MBR voting\"):\n raw_pool = master_pools[str(sample_id)]\n cleaned_pool = []\n for entry in raw_pool:\n if isinstance(entry, dict):\n label = str(entry.get(\"label\", \"\"))\n text = str(entry.get(\"text\", \"\"))\n else:\n label = \"\"\n text = str(entry)\n if label in PUBLIC_SAFE_LABELS:\n cleaned_pool.append(processor.process(text))\n else:\n cleaned_pool.append(text.strip())\n best = mbr.pick(cleaned_pool)\n final_rows.append({\"id\": sample_id, \"translation\": best})\n\n out_path = Path(cfg.output_dir) / \"submission.csv\"\n pd.DataFrame(final_rows).to_csv(out_path, index=False)\n LOGGER.info(\"submission saved to %s\", out_path)\n\n\nif __name__ == \"__main__\":\n run_pipeline()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-19T18:59:51.101422Z","iopub.execute_input":"2026-03-19T18:59:51.101676Z","iopub.status.idle":"2026-03-19T18:59:51.146797Z","shell.execute_reply.started":"2026-03-19T18:59:51.101653Z","shell.execute_reply":"2026-03-19T18:59:51.146151Z"}},"outputs":[],"execution_count":null}]}